Predictor set optimization for collaborative filtering

Abstract

One of the most efficient approaches to create a recommender system is collaborative filtering (CF). CF does not require metadata about users and items, but only interactions between users and items (e.g. ratings), therefore it can be applied in many problem domains. Experience shows that for achieving high accuracy, it is worthwhile to use a blended solution, consisting of many predictors. This paper presents an algorithm for constructing a set of CF predictors so that the overall accuracy of the set is high. The algorithm was tested on the Netflix Prize dataset that contains 100 million ratings.